While methods which classify pixels in an infrared image correctly classify most pixels, incorrect classifications are still assigned to some pixels when the source image was captured by the multi-band imaging system under low light conditions. With information such as, for instance, the illumination spectrum, windshield transmittance, filter transmittance, detector response, and the reflectance of a known material, a theoretical camera intensity for a pixel of that material in the image can be calculated. By correlating the captured camera intensity with the theoretical camera intensity for that material, a pixel can be classified as being that material or not. Theoretically, pixels can be classified correctly. However, in reality, many pixels are wrongly classified due to non-uniform lighting, background fluctuation, and objects such as a shadow. FIG. 15 shows a binary image which has been pixel-classified as to skin using a correlation coefficient method of pixel classification. The binary image of FIG. 15 was generated using a threshold for the correlation coefficient of 0.94 and assigning a value of 1 to a pixel if the correlation coefficient for that pixel was greater than 0.94, and assigning a value of 0 to that pixel if the correlation coefficient was less than 0.94. As shown, many pixels have been wrongly classified as correlating to human skin. As such, post-processing of a pixel-classified binary image is needed to reduce pixel classification error.
Accordingly, what is needed in this art are sophisticated systems and methods for post-processing a multi-spectral image which has already been processed for pixel classification so that the objects in the image can be properly identified and extracted.